用拓扑路由和消息聚合优化细粒度通信

Lukasz Wesolowski, Ramprasad Venkataraman, Abhishek K. Gupta, Jae-Seung Yeom, K. Bisset, Yanhua Sun, Pritish Jetley, T. Quinn, L. Kalé
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引用次数: 21

摘要

超级计算应用程序中的细粒度通信通常由于通信开销高和网络带宽利用率低而限制性能。本文提出了拓扑路由和聚合模块(TRAM),这是一个通过路由和动态组合短消息来优化细粒度通信性能的库。TRAM从应用程序中收集细粒度通信单元,并将它们组合成具有公共中间目的地的聚合消息。它沿着映射到网络物理拓扑的虚拟网格拓扑路由这些消息。TRAM提高了网络带宽利用率,降低了通信开销。它在优化具有全局通信和大消息计数的模式方面特别有效,例如所有对所有和多对多,以及稀疏、不规则、动态或数据依赖的模式。我们通过理论分析和使用基准和科学应用的实验验证来演示TRAM如何提高性能。我们展示了千兆级系统上6倍的通信基准加速和高达4倍的应用程序加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TRAM: Optimizing Fine-Grained Communication with Topological Routing and Aggregation of Messages
Fine-grained communication in supercomputing applications often limits performance through high communication overhead and poor utilization of network bandwidth. This paper presents Topological Routing and Aggregation Module (TRAM), a library that optimizes fine-grained communication performance by routing and dynamically combining short messages. TRAM collects units of fine-grained communication from the application and combines them into aggregated messages with a common intermediate destination. It routes these messages along a virtual mesh topology mapped onto the physical topology of the network. TRAM improves network bandwidth utilization and reduces communication overhead. It is particularly effective in optimizing patterns with global communication and large message counts, such as all-to-all and many-to-many, as well as sparse, irregular, dynamic or data dependent patterns. We demonstrate how TRAM improves performance through theoretical analysis and experimental verification using benchmarks and scientific applications. We present speedups on petascale systems of 6x for communication benchmarks and up to 4x for applications.
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